Rehabilitation Promotes Recovery After Whole Blood–Induced Intracerebral Hemorrhage in Rats
Bibliographic record
Abstract
BACKGROUND: Rehabilitation improves recovery after intracerebral hemorrhage (ICH) caused by collagenase infusion into the striatum of rats by promoting dendritic growth and reducing brain injury in this model. OBJECTIVE: Effective preclinical testing requires multiple models because none, including the collagenase model, perfectly mimics human ICH. Thus, the authors assessed enhanced rehabilitation (ER), a combination of environmental enrichment and task-specific motor training, on skilled reaching, lesion size, and dendritic plasticity after whole blood-induced, striatal ICH. METHODS: Three groups of rats were trained to retrieve food in a reaching task prior to ICH. One group was euthanized at 7 days, whereas 2 groups survived 7 weeks post-ICH. Of the latter, 1 group received 2 weeks of ER starting at 7 days, whereas controls did not. Reaching success was assessed 6 weeks after ICH. Lesion volume and dendritic length and complexity (contralateral striatum) were assessed. RESULTS: The ICH caused reaching deficits that were markedly attenuated by ER as observed previously in the collagenase model. In contrast to that model, there was a time-dependent decline in dendritic length after untreated, whole blood-induced ICH. Furthermore, behavioral recovery was not accompanied by changes in lesion volume or contralateral dendritic morphology. CONCLUSIONS: Converging data from animal models support the use of rehabilitation for ICH patients. However, although rehabilitation effectively promotes behavioral recovery, the mechanisms of action vary by model making it difficult to predict clinical effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".